Machine Learning Engineering

Active Learning

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:14:47+0000

Recommended Reading * Support Vector Machine Active Learning with Applications to Text Classification by Simon Tong and Daphne Koller (2001) * From Theories to Queries: Active Learning in Practice by Burr Settles (2011) * Active Learning by Querying Informative and Representative Examples by Sheng-Jun Huang, Rong Jin, and Zhi-Hua Zhou (2010) * Active Learning Literature Survey by Burr Settles (2010)

Association Rule Learning

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:21:45+0000

Recommended Reading * Association Rules Mining: A Recent Overview by Sotiris Kotsiantis and Dimitris Kanellopoulos (2006) * Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach by Pei et al. (2004) * Mining Frequent Patterns without Candidate Generation by Han et al. (2000) Recommended Watching * Frequent Pattern (FP) growth Algorithm for Association Rule Mining by StudyKorner (2017)

Contents (hardcover)

Anonymous (anonymous@undisclosed.example.com) — 2019-05-15T09:43:41+0000

* Preface * Introduction * What is Machine Learning * Types of Learning * Supervised Learning * Unsupervised Learning * Semi-Supervised Learning * Reinforcement Learning * How Supervised Learning Works * Why the Model Works on New Data

Convolutional Neural Network

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:22:56+0000

Recommended Reading * Convolutional Neural Networks: Architectures, Convolution / Pooling Layers by Andrej Karpathy * Convolutional Neural Networks from the ground up by Alejandro Escontrela * Deep learning by Michael Nielsen

Corrections

Anonymous (anonymous@undisclosed.example.com) — 2020-10-02T21:08:06+0000

Corrections Figure 3.15, the caption should read “Figure 3.15: Oversampling (left) and undersampling (right).” and not “Figure 3.15: Undersampling (left) and oversampling (right).”

DBSCAN and HDBSCAN

Anonymous (anonymous@undisclosed.example.com) — 2019-05-04T22:49:20+0000

Recommended Reading * An extended version of Chapter 9 with the details of HDBSCAN* * How HDBSCAN Works * Comparing Python Clustering Algorithms * Benchmarking Performance and Scaling of Python Clustering Algorithms

Decision Tree Learning

Anonymous (anonymous@undisclosed.example.com) — 2019-04-07T21:46:53+0000

Recommended Reading * An extended version of Chapter 3 with the Appendix describing the C4.5 algorithm * A useful view of decision trees by Ben Kuhn

Determining the Number of Clusters

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:24:10+0000

Recommended Reading * Estimating the number of clusters in a dataset via the gap statistic by Tibshirani et al. (2001) * Cluster Validation by Prediction Strength| by Robert Tibshirani and Guenther Walther (2005)

Ensemble Learning

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:25:02+0000

Recommended Reading * Greedy Function Approximation: A Gradient Boosting Machine by Jerome Friedman (2001) * Ensemble Methods in Machine Learning by Thomas Dietterich (2000) * Ensemble learning on Scholarpedia. * How to explain gradient boosting by Terence Parr and Jeremy Howard. Recommended Watching * Gradient Boosted Decision Trees: Classification by Yandex on Coursera

Gaussian Mixture Model

Anonymous (anonymous@undisclosed.example.com) — 2019-04-23T18:44:40+0000

Animated Illustrations Click to see animation: (Built by Ranjan Piyush.)

Gradient Descent

Anonymous (anonymous@undisclosed.example.com) — 2021-06-10T20:32:58+0000

Resources * Dataset used in the section Recommended Reading * An overview of gradient descent optimization algorithms by Sebastian Ruder Animated Illustrations Click to see animation: (Built by Ranjan Piyush.)

Handling Imbalanced Datasets

Anonymous (anonymous@undisclosed.example.com) — 2019-04-07T21:26:19+0000

Libraries * imbalanced-learn

Hyperparameter Tuning

Anonymous (anonymous@undisclosed.example.com) — 2019-01-23T02:35:14+0000

Recommended Reading * Algorithms for Hyper-Parameter Optimization by Bergstra et al. (NIPS, 2011) * Hyperparameter tuning for machine learning models by Jeremy Jordan * Hyperparameter Optimization in Machine Learning Models by Sayak Paul Recommended Watching * Hyperparameter tuning I on Coursera. Recommended Tools * Hyperopt * Spearmint * TPOT * hyperas

k_means

Anonymous (anonymous@undisclosed.example.com) — 2019-04-28T02:32:54+0000

Animated Illustrations Click to see animation: (Built by Ranjan Piyush.)

Kernel Density Estimation

Anonymous (anonymous@undisclosed.example.com) — 2019-04-23T18:44:59+0000

Animated Illustrations Click to see animation: (Built by Ranjan Piyush.)

Kernel Regression

Anonymous (anonymous@undisclosed.example.com) — 2019-04-23T18:44:51+0000

Recommended Reading * Kernel Regression by Ryan Tibshirani (2014) Recommended Watching * Nonparametric Kernel regression by Anders Munk-Nielsen ( 2016) Animated Illustrations Click to see animation: (Built by Ranjan Piyush.)

Learning to Label Sequences

Anonymous (anonymous@undisclosed.example.com) — 2019-01-11T05:24:37+0000

Recommended Reading * Named Entity Recognition and the Road to Deep Learning by Yves Peirsman * An Introduction to Conditional Random Fields by Charles Sutton and Andrew McCallum (2010) * Named Entity Recognition with Bidirectional LSTM-CNNs by Jason Chiu and Eric Nichols (2015) * Do LSTMs really work so well for PoS tagging? – A replication study by Tobias Horsmann and Torsten Zesch (2017)

Learning to Rank

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T23:52:19+0000

Recommended Reading * LambdaMART Demystified by Tomas Tunys (2015) * From RankNet to LambdaRank to LambdaMART: An Overview by Christopher Burges (2010) * Yahoo! Learning to Rank Challenge Overview by Olivier Chapelle and Yi Chang (2011)

Learning to Recommend

Anonymous (anonymous@undisclosed.example.com) — 2018-11-25T23:08:25+0000

Recommended Reading * Recommender systems survey by Bobadilla et al. (2013) * Recommender systems: from algorithms to user experience by Joseph Konstan and John Riedl (2012) * Recommender Systems by Le et al. (2012) * Neural Collaborative Filtering by He et al. (2017) * Factorization Machines by Steffen Rendle (2010) * Factorization Machines: A New Way of Looking at Machine Learning by Brad Harris (2015) * Collaborative Denoising Auto-Encoders for Top-N Recommender Systems by Wu e…

Metric Learning

Anonymous (anonymous@undisclosed.example.com) — 2019-04-07T21:22:40+0000

Recommended Reading * Distance metric learning, with application to clustering with side-information by Xing et al. (2002) * Distance Metric Learning for Large Margin Nearest Neighbor Classification by Weinberger and Saul (2009) * Tutorial on Metric Learning Aurelien Bellet (2013) * Triangle inequality for positive definite symmetric real matrix Libraries * metric-learn

Multi-Label Classification

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:27:30+0000

Recommended Reading * Multi-Label Classification: An Overview by Grigorios Tsoumakas and Ioannis Katakis (2007) * A k-Nearest Neighbor Based Algorithm for Multi-label Classification by Min-Ling Zhang and Zhi-Hua Zhou (2005) * Combining Instance-Based Learning and Logistic Regression for Multilabel Classification by Weiwei Cheng and Eyke Hullermeier (2009) * A Ranking-based KNN Approach for Multi-Label Classification by Tsung-Hsien Chiang, Hung-Yi Lo, and Shou-De Lin (2012)

One-Class Classification

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:28:22+0000

Recommended Reading * One-class classification by David Martinus Johannes (2001) * Introduction to One-class Support Vector Machines by Roemer Vlasveld (2013) * One-Class SVMs for Document Classification by Larry Manevitz and Malik Yousef (2001) * SVMC: Single-Class Classification With Support Vector Machines by Hwanjo Yu (2003) * A Survey of Recent Trends in One Class Classification by Shehroz Khan and Michael Madden (2010) Code * One-class SVM with non-linear kernel (RBF)

Other Clustering Algorithms

Anonymous (anonymous@undisclosed.example.com) — 2019-08-01T19:56:36+0000

Recommended Reading * A Tutorial on Spectral Clustering by Ulrike von Luxburg (2007) Recommended Watching * Hierarchical Clustering by Stanford University * StatQuest: Hierarchical Clustering by Josh Starmer * Spectral Clustering by Varun Chandola

Principal Component Analysis

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:29:10+0000

Recommended Watching * StatQuest: Principal Component Analysis (PCA), Step-by-Step by Josh Starmer

read_by_professionals_working_at

Anonymous (anonymous@undisclosed.example.com) — 2020-10-26T23:33:28+0000

Hochwertige Waren vom Produzent. Fabrikverkauf. Versand am gleichen Tag. Bis 95 % günstiger als auf dem Markt. Müllsäcke Alle, Sandsäcke, Raschelsäcke, Spänesäcke, Kartonage, Luftpolstertaschen, Maxibrief,

Recurrent Neural Network

Anonymous (anonymous@undisclosed.example.com) — 2020-10-22T02:24:01+0000

Recommended Reading * An extended version of Chapter 6 with RNN unfolding and bidirectional RNN * The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy (2015) * Recurrent Neural Networks and LSTM by Niklas Donges (2018) * Understanding LSTM Networks by Christopher Olah (2015) * Introduction to RNNs by Denny Britz (2015) * Implementing a RNN with Python, Numpy and Theano by Denny Britz (2015) * Backpropagation Through Time and Vanishing Gradients by Denny B…

Regularization

Anonymous (anonymous@undisclosed.example.com) — 2019-05-06T23:02:23+0000

Recommended Reading * An extended version of Chapter 5 with the comparison of L1 and L2 regularization

Reinforcement Learning

Anonymous (anonymous@undisclosed.example.com) — 2021-07-28T08:18:54+0000

Recommended Reading * Reinforcement Learning: An Introduction, Second Edition by Sutton and Barto (2018) Lectures * Reinforcement Learning Course by Deep Mind and University College London (2018) * Stanford CS234: Reinforcement Learning by Stanford University (2019)

scikit_map

Anonymous (anonymous@undisclosed.example.com) — 2019-10-17T18:06:50+0000

Self-Supervised Learning: Word Embeddings

Anonymous (anonymous@undisclosed.example.com) — 2018-11-28T00:16:21+0000

Recommended Reading * word2vec Parameter Learning Explained by Xin Rong (2016) * Language Models, Word2Vec, and Efficient Softmax Approximations by Rohan Varma (2017)

Semi-Supervised Learning

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:30:19+0000

Recommended Reading * Introduction to Semi-Supervised Learning with Ladder Networks by Rinu Boney (2016) * Semi-Supervised Learning with Ladder Networks by Antti Rasmus et al. (2015) * Semi-Supervised Learning Literature Survey * Temporal Ensembling for Semi-Supervised Learning by Xiaojin Zhu (2008), a review of methods from the pre-deep learning era * Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning by Mehdi Sajjadi, Mehran Javanmardi…

Sequence-to-Sequence Learning

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:30:42+0000

Recommended Reading * Sequence to Sequence Learning with Neural Networks by Ilya Sutskever, Oriol Vinyals, and Quoc Le (2014) * Deep Learning for NLP Best Practices by Sebastian Ruder (2017) Tutorials * A ten-minute introduction to sequence-to-sequence learning in Keras by Francois Chollet (2017), Oriol Vinyals and Quoc Le

start

Anonymous (anonymous@undisclosed.example.com) — 2026-01-28T08:26:17+0000

This is the supporting wiki for the book Machine Learning Engineering by Andriy Burkov. “If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book.” —Cassie Kozyrkov, Chief Decision Scientist at

Support Vector Machine

Anonymous (anonymous@undisclosed.example.com) — 2019-04-07T21:46:28+0000

Recommended Reading * An extended version of Chapter 3 with the Appendix with the derivation of the SVM dual * Support Vector Machines Explained by Tristan Fletcher (2008) * Kernel Methods and SVMs by Justin Domke * An Idiot's guide to Support vector machines (SVMs) by R. Berwick

Why Does Model Work on New Data

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:32:57+0000

Recommended Reading * Overview of the Probably Approximately Correct (PAC) Framework by David Haussler * Chapter 7 of Machine Learning by Tom Mitchell (1997) Recommended Watching * PAC Learning by Georgia Tech/Udacity (2015).

Zero-Shot Learning

Anonymous (anonymous@undisclosed.example.com) — 2018-11-22T00:33:19+0000

Recommended Reading * Zero-Shot Learning by Convex Combination of Semantic Embeddings by Norouzi et al. (2014) * Zero-Shot Learning Through Cross-Modal Transfer by Socher et al. (2013) * Learning a Deep Embedding Model for Zero-Shot Learning by Li Zhang, Tao Xiang, and Shaogang Gong (2017) * Semantic Autoencoder for Zero-Shot Learning by Elyor Kodirov, Tao Xiang, and Shaogang Gong (2017) * Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly by Xian et al…